Merge pull request #64 from omarbelkady/Numpy

Useful Info About Numpy Lib

Author: usrbinomarbash

Numpy/NumpyNdArrayObj.py

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+import numpy as np
+alpha = np.array([1,2,3])
+print(alpha)
+print("\n\n\n")
+a = np.arange(0,60,5) #1d array
+a = a.reshape(3,4) #2d array

Numpy/README.md

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+## Numpy
+- Python library used heavily in ML and DS to perform mathematical and logical operations on arrays
+
+
+### How To Install
+```bash
+sudo apt-get install python-numpy 
+```
+
+### Data Types
+- Boolean -> bool
+- Integer(default) -> int_...same as C's long data type
+- Integer(C family) -> intc ... same as C's int
+- intp->: Integer used for indexing (same as C ssize_t; normally either int32 or int64)
+- int8-> integer
+- int16 -> integer with a bigger range than int8
+- int32 -> integer with a bigger range than int16
+- int64 -> integer with a bigger range than int32
+- uint8 -> unsigned int
+- uint16 -> unsigned int
+- uint32 -> unsigned int
+- uint64 -> unsigned int
+- float_ -> floating data type
+- float16 -> floating data type has a bigger range and is more precise than a float
+- float32 -> floating data type has a bigger range and is more precise than a float16
+- float64 -> floating data type has a bigger range and is more precise than a float32
+- complex_ -> complex data type 
+- complex64 -> complex data type with more precision than an ordinary complex
+- complex128 -> complex data type with more prevision than a complex64
+
+### Character code for data type in Numpy
+
+1. 'b': boolean
+2. 'i': signed integer
+3. 'u': unsigned integer
+4. 'f': floating point
+5. 'c': complex floating point
+6. 'm': time delta
+7. 'M': datetime
+8. 'O': Python Objects
+9. 'S', 'a': String
+10. 'U': Unicode
+
+11. 'V': raw data
+
+
+### How To resize an ndimensional array
+```python
+import numpy as np 
+
+a = np.array([[2,4,9],[3,5,7]]) #this is a 2x3 array
+a.shape = (3,2) # it will now become a 3x2 aka transpose the array
+
+print a 
+#way number 2
+m = np.arange(24) #an array of 24 elements aka 0 through 23 
+a.ndim  
+
+# now reshape its 2 separate arrays, 4 rows, 3 columns
+b = a.reshape(2,4,3) 
+print b 
+```
+
+### Array Indexing
+```python
+import numpy as np 
+a = np.arange(10)  # fill array with 10 elements
+s = slice(2,7,2)  # start at 2 stop 7 step 2 aka 2,4,6
+print a[s]
+```
+
+### How To Iterate Through An Array Using a range-style built-in function of numpy
+```python
+import numpy as np
+ftn = np.arange(0,60,5) # start at 0 stop at 60 step 5 numbers
+ftn = ftn.reshape(3,4) # turn the array into 2d
+
+print 'Original array is:'
+print ftn
+print '\n'
+
+# Transpose of the array
+print 'Transpose of Array:' 
+b = ftn.T 
+print b 
+print '\n'  
+
+print 'Modified array is:'
+for x in np.nditer(b):
+   print x
+```